eva-unit-01 · Open weights
EVA-LLaMA-3.33-70B-v0.1
A RP/storywriting specialist model, full-parameter finetune of Llama-3.3-70B-Instruct on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and flavor of the resulting model.
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $2.01
- Output
- $2.01
Reference prices as published by the model provider. API example
Specifications
- Context window
- 32.8K
- Max output
- —
- Average output (7 days)
- 459 tokens
- Longer than 45% of 468 models
Side by side
EVA-LLaMA-3.33-70B-v0.1 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| EVA-LLaMA-3.33-70B-v0.1eva-unit-01This model | GPT-OSS 120B TEEOpenAIRemove | |
|---|---|---|
| Provider | eva-unit-01 | OpenAI |
| Discovery tier | Open weights | Open weights |
| Added | Sep 25, 2025 | Jan 1, 2024 |
| Limits | ||
| Context window | 32.77K | 131.07KBest |
| Max output | — | 16.38K |
| Average output | 459 tokens | 174 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $2.01 | $2.00Best |
| Output | $2.01 | $2.00Best |
| Cache read | — | $2.00 |
| Performance | ||
| Throughput | 17.5 tok/s | — |
| Time to first token | 8.6 s | — |
| Request success (24h) | 25.5% | — |
| Benchmark indexes | ||
| Intelligence Index | — | 11.6 |
| Coding Index | — | 30.4 |
| Math Index | — | 93.4 |
| Capabilities | ||
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- EVA-LLaMA-3.33-70B-v0.1
- GPT-OSS 120B TEE
Intelligence Index
- EVA-LLaMA-3.33-70B-v0.1—
- GPT-OSS 120B TEE11.6
Indexes
Coding Index
Composite of the coding evaluations
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 30.4
Math Index
Composite of the math evaluations
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 93.4
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 0.2%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 0.0%
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 608 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 4.0%
AA-LCRv1.1
Reasoning across long documents
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 52.0%
MLCR-AA
Long-context reasoning over medical records
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 1.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 19.6%
GPQA Diamond
Graduate-level science questions
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 78.2%
MMLU-Pro
Multi-subject knowledge and reasoning
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 80.8%
IFBench
Following precise output instructions
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 69.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 0.0%
SciCode
Scientific computing in Python
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 34.0%
LiveCodeBench
Recent competitive programming problems
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 87.8%
Math
AIME 2025
Competition mathematics, 2025 problems
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 93.4%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 23.5%
τ²-Bench Telecom
Tool use in a customer-support simulation
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 65.8%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- EVA-LLaMA-3.33-70B-v0.1: —
- GPT-OSS 120B TEE: 52.0%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 17.5 tok/s
- Faster than 11% of 254 models
- Time to first token
- 8.6 s
- Quicker than 14% of 254 models
- Request success (24h)
- 25.5%
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)eva-unit-01
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